A grouping-based strategy to improve the effectiveness of fault localization techniques

Fault localization is one of the most expensive activities of program debugging, which is why the recent years have witnessed the development of many different fault localization techniques. This paper proposes a groupingbased strategy that can be applied to various techniques in order to boost their fault localization effectiveness. The applicability of the strategy is assessed over - Tarantula and a Radial Basis Function neural network-based technique; across three different sets of programs (the Siemens suite, grep and gzip). Results are suggestive that the grouping-based strategy is capable of significantly improving the fault localization effectiveness and is not limited to any particular fault localization technique. The proposed strategy does not require any additional information than what was already collected as input to the fault localization technique, and does not require the technique to be modified in any way.